arXiv papers from June 2023

Sadegh Chegini, Mahmoud Zarepour

An important functional of Poisson random measure is the negative binomial process (NBP). We use NBP to introduce a generalized Poisson-Kingman distribution and its corresponding random discrete probability measure. This random discrete probability measure provides a new set of priors with more flexibility in nonparametric Bayesian models. It is shown how th

B. A. Levinstein, Daniel A. Herrmann

We consider the questions of whether or not large language models (LLMs) have beliefs, and, if they do, how we might measure them. First, we evaluate two existing approaches, one due to Azaria and Mitchell (2023) and the other to Burns et al. (2022). We provide empirical results that show that these methods fail to generalize in very basic ways. We then argu

Xianjun Han, Qianqian Chen, Zhaoyang Xie, Xuejun Li

The accurate segmentation of medical images is a crucial step in obtaining reliable morphological statistics. However, training a deep neural network for this task requires a large amount of labeled data to ensure high-accuracy results. To address this issue, we propose using progressive text prompts as prior knowledge to guide the segmentation process. Our

Pierre Nzabahimana, Pawel Danielewicz

In heavy-ion collisions, low relative-velocity two-particle correlations have been a tool for assessing space-time characteristics of particle emission. Those characteristics may be cast in the form of a relative emission source related to the correlation function through the Koonin-Pratt (KP) convolution formula that involves the relative wave-function for

Fred Ghanem, Kirti M. Yenkie

Single-use anion-exchange resins can reduce hazardous chromates to safe levels in drinking water. However, since most process control strategies monitor effluent concentrations, detection of any chromate leakage leads to premature resin replacement. Furthermore, variations in the inlet chromate concentration and other process conditions make process control

Vivek Srikumar, Dan Roth

Over the years, integer linear programs have been employed to model inference in many natural language processing problems. This survey is meant to guide the reader through the process of framing a new inference problem as an instance of an integer linear program and is structured as a collection of recipes. At the end, we will see two worked examples to ill

Eric J. Sung, Andre G. Campos, Hartmut Abele, Denys I. Bondar

The theory of entropic gravity conjectures that gravity emerges thermodynamically rather than being a fundamental force. One of the main criticisms of entropic gravity is that it would lead to quantum massive particles losing coherence in free fall, which is not observed experimentally. This criticism was refuted in [Phys. Rev. Res. 3, 033065 (2021)], where

Raghuveer Peri, Seyed Omid Sadjadi, Daniel Garcia-Romero

Despite its broad practical applications such as in fraud prevention, open-set speaker identification (OSI) has received less attention in the speaker recognition community compared to speaker verification (SV). OSI deals with determining if a test speech sample belongs to a speaker from a set of pre-enrolled individuals (in-set) or if it is from an out-of-s

Robert Kleinberg, Renato Paes Leme, Jon Schneider, Yifeng Teng

We consider the problem of evaluating forecasts of binary events whose predictions are consumed by rational agents who take an action in response to a prediction, but whose utility is unknown to the forecaster. We show that optimizing forecasts for a single scoring rule (e.g., the Brier score) cannot guarantee low regret for all possible agents. In contrast,

Yun Chen, Nuria González-Prelcic, Takayuki Shimizu, Hongsheng Lu

One strategy to obtain user location information in a wireless network operating at millimeter wave (mmWave) is based on the exploitation of the geometric relationships between the channel parameters and the user position. These relationships can be built from the line-of-sight (LOS) path and first-order reflections, or purely first-order reflections, requir

Arash Yavari

For a given class of materials, universal displacements are those displacements that can be maintained for any member of the class by applying only boundary tractions. In this paper we study universal displacements in compressible anisotropic linear elastic solids reinforced by a family of inextensible fibers. For each symmetry class and for a uniform distri

Jianchao Ji, Zelong Li, Shuyuan Xu, Max Xiong

Causal reasoning and logical reasoning are two important types of reasoning abilities for human intelligence. However, their relationship has not been extensively explored under machine intelligence context. In this paper, we explore how the two reasoning abilities can be jointly modeled to enhance both accuracy and explainability of machine learning models.

Fabio Sozio, Arash Yavari

In this paper a geometric field theory of dislocation dynamics and finite plasticity in single crystals is formulated. Starting from the multiplicative decomposition of the deformation gradient into elastic and plastic parts, we use Cartan's moving frames to describe the distorted lattice structure via differential $1$-forms. In this theory the primary field

Marco Clementi, Edgars Nitiss, Elena Durán-Valdeiglesias, Sofiane Belahsene

Second-harmonic generation allows for coherently bridging distant regions of the optical spectrum, with applications ranging from laser technology to self-referencing of frequency combs. However, accessing the nonlinear response of a medium typically requires high-power bulk sources, specific nonlinear crystals, and complex optical setups, hindering the path

Ankita Pasad, Chung-Ming Chien, Shane Settle, Karen Livescu

Many self-supervised speech models (S3Ms) have been introduced over the last few years, improving performance and data efficiency on various speech tasks. However, these empirical successes alone do not give a complete picture of what is learned during pre-training. Recent work has begun analyzing how S3Ms encode certain properties, such as phonetic and spea

Weijie Xu, Jinjin Zhao, Francis Iannacci, Bo Wang

Generative modeling has been used frequently in synthetic data generation. Fairness and privacy are two big concerns for synthetic data. Although Recent GAN [\cite{goodfellow2014generative}] based methods show good results in preserving privacy, the generated data may be more biased. At the same time, these methods require high computation resources. In this

Shadi Shaqaqha

The representations of various color Lie superalgebras by Hilbert series are the main topic of this work. The Color Lie superalgebras appear in various branches of mathematics (e.g., topology, algebraic groups, etc.). They are generalized Lie superalgebras. A generating function known as the Hilbert series of color Lie superalgebras which encodes crucial kno

Satya Prakash Pradhan, Arash Yavari

In this paper we formulate a geometric nonlinear theory of the mechanics of accreting-ablating bodies. This is a generalization of the theory of accretion mechanics of Sozio and Yavari (2019). More specifically, we are interested in large deformation analysis of bodies that undergo a continuous and simultaneous accretion and ablation on their boundaries whil

S. S. Elgueta, N. Matsunaga, M. Jian, D. Taniguchi

Newly-developed spectrographs with increased resolving powers, particularly those covering the near-IR range, allow the characterization of more and more absorption lines in stellar spectra. This includes the identification and confirmation of absorption lines and the calibration of oscillator strengths. In this study, we provide empirical values of loggf ba

Adrian Stando, Mustafa Cavus, Przemysław Biecek

Imbalanced data poses a significant challenge in classification as model performance is affected by insufficient learning from minority classes. Balancing methods are often used to address this problem. However, such techniques can lead to problems such as overfitting or loss of information. This study addresses a more challenging aspect of balancing methods

Oceane Bel, Joonseok Kim, William J Hofer, Manisha Maharjan

Resilience assessment is a critical requirement of a power grid to maintain high availability, security, and quality of service. Most grid research work that is currently pursued does not have the capability to have hardware testbeds. Additionally, with the integration of distributed energy resources, the attack surface of the grid is increasing. This increa

Prathamesh Saraf, Mustafa Shaikh, Myron Phan

Convex optimization is crucial in controlling legged robots, where stability and optimal control are vital. Many control problems can be formulated as convex optimization problems, with a convex cost function and constraints capturing system dynamics. Our review focuses on active balancing problems and presents a general framework for formulating them as sec

Zhuonan Hao, Sangmin Lim, M. Khalid Jawed

Multi-flagellated bacteria utilize the hydrodynamic interaction between their filamentary tails, known as flagella, to swim and change their swimming direction in low Reynolds number flow. This interaction, referred to as bundling and tumbling, is often overlooked in simplified hydrodynamic models such as Resistive Force Theories (RFT). However, for the deve

Zizheng Pan, Jing Liu, Haoyu He, Jianfei Cai

Large pretrained plain vision Transformers (ViTs) have been the workhorse for many downstream tasks. However, existing works utilizing off-the-shelf ViTs are inefficient in terms of training and deployment, because adopting ViTs with individual sizes requires separate trainings and is restricted by fixed performance-efficiency trade-offs. In this paper, we a

Kevin Wils, Boyang Chen

With the advent of novel quantum computing technologies, and the knowledge that such technology might be used to fundamentally change computing applications, a prime opportunity has presented itself to investigate the practical application quantum computing. The goal of this research is to consider one of the most basic forms of mechanical structure, namely

Christopher Getschmann, Florian Echtler

Cameras provide a vast amount of information at high rates and are part of many specialized or general-purpose devices. This versatility makes them suitable for many interaction scenarios, yet they are constrained by geometry and require objects to keep a minimum distance for focusing. We present the LensLeech, a soft silicone cylinder that can be placed dir

Rodrigo Raya

We study the satisfiability problem of symbolic finite automata and decompose it into the satisfiability problem of the theory of the input characters and the monadic second-order theory of the indices of accepted words. We use our decomposition to obtain tight computational complexity bounds on the decision problem for this automata class and an extension t

Maciej Pankiewicz, Ryan S. Baker

Addressing the challenge of generating personalized feedback for programming assignments is demanding due to several factors, like the complexity of code syntax or different ways to correctly solve a task. In this experimental study, we automated the process of feedback generation by employing OpenAI's GPT-3.5 model to generate personalized hints for student

Xiang Xu, Pradeep Kumar Jayaraman, Joseph G. Lambourne, Karl D. D. Willis

This paper presents a novel generative model for Computer Aided Design (CAD) that 1) represents high-level design concepts of a CAD model as a three-level hierarchical tree of neural codes, from global part arrangement down to local curve geometry; and 2) controls the generation or completion of CAD models by specifying the target design using a code tree. C

William L. Blair

We construct solutions to the Schwarz boundary value problem on the unit disk and the upper half-plane when the boundary condition is with respect to boundary values in the sense of distributions.

Thatchaphol Saranurak, Wuwei Yuan

We give the first almost-linear time algorithm for computing the \emph{maximal $k$-edge-connected subgraphs} of an undirected unweighted graph for any constant $k$. More specifically, given an $n$-vertex $m$-edge graph $G=(V,E)$ and a number $k = \log^{o(1)}n$, we can deterministically compute in $O(m+n^{1+o(1)})$ time the unique vertex partition $\{V_{1},\d

Josh Pollock, Catherine Mei, Grace Huang, Elliot Evans

Diagrams are essential tools for problem-solving and communication as they externalize conceptual structures using spatial relationships. But when picking a diagramming framework, users are faced with a dilemma. They can either use a highly expressive but low-level toolkit, whose API does not match their domain-specific concepts, or select a high-level typol

Shafee Farzanian, Imtiaz Shozib, Nikhil Sivadas, Valentina Lacivita

Despite ongoing efforts aimed at increasing energy density in all-solid-state-batteries, the optimal composite cathode morphology, which requires minimal volume change, small void development, and good interfacial contact, remains a significant concern within the community. In this work, we focus on the theoretical investigation of the above-mentioned mechan

Sibylle Marcotte, Rémi Gribonval, Gabriel Peyré

Understanding the geometric properties of gradient descent dynamics is a key ingredient in deciphering the recent success of very large machine learning models. A striking observation is that trained over-parameterized models retain some properties of the optimization initialization. This "implicit bias" is believed to be responsible for some favorable prope

Hari Venugopalan, Kaustav Goswami, Zainul Abi Din, Jason Lowe-Power

Device fingerprinting leverages attributes that capture heterogeneity in hardware and software configurations to extract unique and stable fingerprints. Fingerprinting countermeasures attempt to either present a uniform fingerprint across different devices through normalization or present different fingerprints for the same device each time through obfuscati

Patrick Emami, Abhijeet Sahu, Peter Graf

Short-term forecasting of residential and commercial building energy consumption is widely used in power systems and continues to grow in importance. Data-driven short-term load forecasting (STLF), although promising, has suffered from a lack of open, large-scale datasets with high building diversity. This has hindered exploring the pretrain-then-fine-tune p

Ruoqi Zhang, Jens Sjölund

Traditional reinforcement learning methods optimize agents without considering safety, potentially resulting in unintended consequences. In this paper, we propose an optimal actor-free policy that optimizes a risk-sensitive criterion based on the conditional value at risk. The risk-sensitive objective function is modeled using an input-convex neural network

William L. Blair

We develop a representation of the second kind for certain Hardy classes of solutions to nonhomogeneous Cauchy-Riemann equations and use it to show that boundary values in the sense of distributions of these functions can be represented as the sum of an atomic decomposition and an error term. We use the representation to show continuity of the Hilbert transf

K. A. Hunnestad, H. Das, C. Hatzoglou, M. Holtz

Oxide heterostructures exhibit a vast variety of unique physical properties. Examples are unconventional superconductivity in layered nickelates and topological polar order in (PbTiO$_3$)$_n$/(SrTiO$_3$)$_n$ superlattices. Although it is clear that variations in oxygen content are crucial for the electronic correlation phenomena in oxides, it remains a major

R. L. H. Freire, F. Crasto de Lima, A. Fazzio

2D materials present an interesting platform for device designs. However, oxidation can drastically change the system's properties, which need to be accounted for. Through {\it ab initio} calculations, we investigated freestanding and SiC-supported As, Sb, and Bi mono-elemental layers. The oxidation process occurs through an O$_2$ spin-state transition, acco

Peter Benner, Kathryn Lund, Jens Saak

The race for the most efficient, accurate, and universal algorithm in scientific computing drives innovation. At the same time, this healthy competition is only beneficial if the research output is actually comparable to prior results. Fairly comparing algorithms can be a complex endeavor, as the implementation, configuration, compute environment, and test p

Jared Stewart, Mayya Tokman, Fabrizio Bisetti, Valentin Dallerit

Computational chemical combustion problems are known to be stiff, and are typically solved with implicit time integration methods. A novel exponential time integrator, EPI3V, is introduced and applied to a spatially homogeneous isobaric reactive mixture. Three chemical mechanism of increasing complexity are considered, and in two cases the novel method can p

Vasilisa Bashlovkina, Riley Matthews, Zhaobin Kuang, Simon Baumgartner

We study the ability of transformer-based language models (LMs) to understand social media language. Social media (SM) language is distinct from standard written language, yet existing benchmarks fall short of capturing LM performance in this socially, economically, and politically important domain. We quantify the degree to which social media language diffe

Giuseppe Alessio D'Inverno, Simone Brugiapaglia, Mirco Ravanelli

Graph Neural Networks (GNNs) have emerged as a powerful tool for data-driven learning on various graph domains. They are usually based on a message-passing mechanism and have gained increasing popularity for their intuitive formulation, which is closely linked to the Weisfeiler-Lehman (WL) test for graph isomorphism to which they have been proven equivalent

James Akl, Yash Patil, Chinmay Todankar, Berk Calli

The automation of key processes in metal cutting would substantially benefit many industries such as manufacturing and metal recycling. We present a vision-based control scheme for automated metal cutting with oxy-fuel torches, an established cutting medium in industry. The system consists of a robot equipped with a cutting torch and an eye-in-hand camera ob

N Harsha Vardhan, Manav Chaudhary

Sentence-level relation extraction (RE) aims to identify the relationship between 2 entities given a contextual sentence. While there have been many attempts to solve this problem, the current solutions have a lot of room to improve. In this paper, we approach the task of relationship extraction in the financial dataset REFinD. Our approach incorporates type

Jan Trieschmann, Luca Vialetto, Tobias Gergs

Machine learning has had an enormous impact in many scientific disciplines. Also in the field of low-temperature plasma modeling and simulation it has attracted significant interest within the past years. Whereas its application should be carefully assessed in general, many aspects of plasma modeling and simulation have benefited substantially from recent de

Shiyu Yuan, Carlo Lipizzi

Information extraction (IE) plays very important role in natural language processing (NLP) and is fundamental to many NLP applications that used to extract structured information from unstructured text data. Heuristic-based searching and data-driven learning are two main stream implementation approaches. However, no much attention has been paid to document g

Zikai Lin, Yajuan Si, Jian Kang

Image-on-scalar regression has been a popular approach to modeling the association between brain activities and scalar characteristics in neuroimaging research. The associations could be heterogeneous across individuals in the population, as indicated by recent large-scale neuroimaging studies, e.g., the Adolescent Brain Cognitive Development (ABCD) study. T

Sagar Ghorai, Rafael Martinho Vieira, Vitalii Shtender, Erna K. Delczeg-Czirjak

The search for energy-efficient and environmentally friendly cooling technologies is a key driver for the development of magnetic refrigeration based on the magnetocaloric effect (MCE). This phenomenon arises from the interplay between magnetic and lattice degrees of freedom that is strong in certain materials, leading to a change in temperature upon applica

Reza Mohammadi, Marit Schoonhoven, Lucas Vogels, S. Ilker Birbil

Bayesian methods for learning Gaussian graphical models offer a principled framework for quantifying model uncertainty and incorporating prior knowledge. However, their scalability is constrained by the computational cost of jointly exploring graph structures and precision matrices. To address this challenge, we perform inference directly on the graph by int

Haikuo Yang, Luo Luo, Chris Junchi Li, Michael I. Jordan

We present a method for solving general nonconvex-strongly-convex bilevel optimization problems. Our method -- the \emph{Restarted Accelerated HyperGradient Descent} (\texttt{RAHGD}) method -- finds an $\epsilon$-first-order stationary point of the objective with $\tilde{\mathcal{O}}(\kappa^{3.25}\epsilon^{-1.75})$ oracle complexity, where $\kappa$ is the co

Eleftherios Triantafyllidis, Fernando Acero, Zhaocheng Liu, Zhibin Li

Solving long sequential tasks poses a significant challenge in embodied artificial intelligence. Enabling a robotic system to perform diverse sequential tasks with a broad range of manipulation skills is an active area of research. In this work, we present a Hybrid Hierarchical Learning framework, the Robotic Manipulation Network (ROMAN), to address the chal

Nils Kohl, Stephen F. McCormick, Rasmus Tamstorf

Block Floating Point (BFP) arithmetic is currently seeing a resurgence in interest because it requires less power, less chip area, and is less complicated to implement in hardware than standard floating point arithmetic. This paper explores the application of BFP to mixed- and progressive-precision multigrid methods, enabling the solution of linear elliptic

Ali Ayub, Jainish Mehta, Zachary De Francesco, Patrick Holthaus

Continual learning (CL) has emerged as an important avenue of research in recent years, at the intersection of Machine Learning (ML) and Human-Robot Interaction (HRI), to allow robots to continually learn in their environments over long-term interactions with humans. Most research in continual learning, however, has been robot-centered to develop continual l

Yaxiong Lei, Shijing He, Mohamed Khamis, Juan Ye

In recent years we have witnessed an increasing number of interactive systems on handheld mobile devices which utilise gaze as a single or complementary interaction modality. This trend is driven by the enhanced computational power of these devices, higher resolution and capacity of their cameras, and improved gaze estimation accuracy obtained from advanced

Matthias Schmidt, Stephanie M. Bohaichuk, Vijin Venu, Florian Christaller

Rydberg atom-based radio frequency electromagnetic field sensors are drawing wide-spread interest because of their unique properties, such as small size, dielectric construction, and self-calibration. These photonic sensors use lasers to prepare atoms and read out the atomic response to a radio frequency electromagnetic field based on electromagnetically ind

Thomas Bitoun

Let Z be the germ of a complex hypersurface isolated singularity of equation f, with Z at least of dimension 2. We consider the family of analytic D-modules generated by the powers of 1/f and describe it in terms of the pole order filtration on the de Rham cohomology of the complement of {f=0} in the neighborhood of the singularity.

Aaron Mueller, Kanika Narang, Lambert Mathias, Qifan Wang

Large language models show impressive results on few-shot NLP tasks. However, these models are memory and computation-intensive. Meta-training allows one to leverage smaller models for few-shot generalization in a domain-general and task-agnostic manner; however, these methods alone results in models that may not have sufficient parameterization or knowledge

Hao Zheng, Tao Zhou, Dina Sheyfer, Jieun Kim

Relaxor ferroelectrics are a class of materials that are widely perceived as deriving their exotic properties from structural heterogeneities. Understanding the microscopic origin of the superior electromechanical response requires knowledge not only concerning the formation of polar nanodomains (PNDs) built from individual atoms but more importantly the spa

Vivek Myers, Andre He, Kuan Fang, Homer Walke

Our goal is for robots to follow natural language instructions like "put the towel next to the microwave." But getting large amounts of labeled data, i.e. data that contains demonstrations of tasks labeled with the language instruction, is prohibitive. In contrast, obtaining policies that respond to image goals is much easier, because any autonomous trial or

Emily Heath, Ryan R. Martin, Chris Wells

How many copies of a fixed odd cycle, $C_{2m+1}$, can a planar graph contain? We answer this question asymptotically for $m\in\{2,3,4\}$ and prove a bound which is tight up to a factor of $3/2$ for all other values of $m$. This extends the prior results of Cox--Martin and Lv et al. on the analogous question for even cycles. Our bounds result from a reduction

Vladimir Kolmogorov

Currently, the best known tradeoff between approximation ratio and complexity for the Sparsest Cut problem is achieved by the algorithm in [Sherman, FOCS 2009]: it computes $O(\sqrt{(\log n)/\varepsilon})$-approximation using $O(n^\varepsilon\log^{O(1)}n)$ maxflows for any $\varepsilon\in[\Theta(1/\log n),\Theta(1)]$. It works by solving the SDP relaxation o

Ali Ayub, Chrystopher L. Nehaniv, Kerstin Dautenhahn

For robots to assist users with household tasks, they must first learn about the tasks from the users. Further, performing the same task every day, in the same way, can become boring for the robot's user(s), therefore, assistive robots must find creative ways to perform tasks in the household. In this paper, we present a cognitive architecture for a househol

Jilang Miao, Miaomiao Jin

This paper presents an accurate S$_N$ solver for slab geometry. For constant cross-section regions, it gives accurate angular fluxes without need of fine meshes or approximation of solution forms.

Prabin Sharma, Kisan Thapa, Dikshya Thapa, Prastab Dhakal

Artificial intelligence is gaining traction in more ways than ever before. The popularity of language models and AI-based businesses has soared since ChatGPT was made available to the general public via OpenAI. It is becoming increasingly common for people to use ChatGPT both professionally and personally. Considering the widespread use of ChatGPT and the re

Don-Roberts Emenonye, Anik Sarker, Alan T. Asbeck, Harpreet S. Dhillon

This paper is the first to introduce the idea of using reconfigurable intelligent surfaces (RISs) as passive devices that measure the position and orientation of certain human body parts over time. In this paper, we investigate the possibility of utilizing the available geometric information provided by on-body RISs that reflect signals from an off-body tran

David C. Garrett, Jinhua Xu, Yousuf Aborahama, Geng Ku

Ultrasonography is a vital component of modern clinical care, with handheld probes routinely used for diagnostic imaging and procedural guidance. However, handheld ultrasound imaging is limited by factors such as the partial-cross-sectional field of view, operator dependency, contact-induced distortion, and lack of transmission contrast. Here, we demonstrate

Grigor Sargsyan

The goal of this paper is to present an approach to Hod Pair Capturing (HPC). $HPC$ is the most outstanding open problem of descriptive inner model theory. More specifically, we introduce two principles, the Direct Limit Independence and the Bounded Direct Limits, and show that they together imply HPC.

Zhexiong Liu, Cris Benge, Siduo Jiang

An essential aspect of prioritizing incident tickets for resolution is efficiently labeling tickets with fine-grained categories. However, ticket data is often complex and poses several unique challenges for modern machine learning methods: (1) tickets are created and updated either by machines with pre-defined algorithms or by engineers with domain expertis

Ollie Thakar

We introduce a new method of detecting when the fundamental group of a Dehn surgery on a knot admits a left-ordering, a method which is particularly useful for 2-bridge knots. As an illustration of this method, we show that all Dehn surgeries on the knot $6_2$ with slope in the interval $(-4, 8)\cap\mathbb{Q}$ have left-orderable fundamental groups by exhibi

Eduardo V. L. Barboza, Paulo R. Lisboa de Almeida, Alceu de Souza Britto, Rafael M. O. Cruz

Data normalization is an essential task when modeling a classification system. When dealing with data streams, data normalization becomes especially challenging since we may not know in advance the properties of the features, such as their minimum/maximum values, and these properties may change over time. We compare the accuracies generated by eight well-kno

Miaomiao Jin, Jilang Miao

The concentration of radiation-induced point defects in general materials under irradiation is commonly described by the point defect kinetics equations based on rate theory. However, the parametric uncertainty in describing the rate constants of competing physical processes such as recombination and loss to sinks can lead to a large uncertainty in predictin

Uma Meleti, Abolfazl Razi

This research paper addresses the challenge of detecting obscured wildfires (when the fire flames are covered by trees, smoke, clouds, and other natural barriers) in real-time using drones equipped only with RGB cameras. We propose a novel methodology that employs semantic segmentation based on the temporal analysis of smoke patterns in video sequences. Our

Raluca M. Balan, Jingyu Huang, Xiong Wang, Panqiu Xia

In this article, we consider the stochastic wave equation in spatial dimension $d=1$, with linear term $\sigma(u)=u$ multiplying the noise. This equation is driven by a Gaussian noise which is white in time and fractional in space with Hurst index $H \in (\frac{1}{4},\frac{1}{2})$. First, we prove that the solution is strictly stationary and ergodic in the s

Hooman Davoudiasl, Roman Marcarelli, Ethan T. Neil

The Electron-Ion Collider (EIC) provides unique opportunities in searching for new physics through its high center of mass energy and coherent interactions of large nuclei. We examine how light weakly interacting vector bosons from a variety of models can be discovered or constrained, over significant parts of their parameter space, through clean displaced v

Harnoor Dhingra, Preetiha Jayashanker, Sayali Moghe, Emma Strubell

Large Language Models (LLMs) are trained primarily on minimally processed web text, which exhibits the same wide range of social biases held by the humans who created that content. Consequently, text generated by LLMs can inadvertently perpetuate stereotypes towards marginalized groups, like the LGBTQIA+ community. In this paper, we perform a comparative stu

Mahan Mohseni, Hassan Allami, Daniel Miravet, David J. Gayowsky

We present here a theory of Majorana excitons, photo-excited conduction electron-valence band hole pairs, interacting with Majorana Fermions in a Kitaev chain of semiconductor quantum dots embedded in a nanowire. Using analytical tools and exact diagonalisation methods we identify the presence of Majorana Zero Modes in the nanowire absorption spectra.

Ian L. V. Roque, Nima Razavi-Ghods, Steven H. Carey, John A. Ely

We detail the the REACH radiometric system designed to enable measurements of the 21-cm neutral hydrogen line. Included is the radiometer architecture and end-to-end system simulations as well as a discussion of the challenges intrinsic to highly-calibratable system development. Following this, we share laboratory results based on the calculation of noise wa

Paul Barry

We use the link between Jacobi continued fractions and the generating functions of certain moment sequences to study some simple transformations on them. In particular, we define and study a transformation that is appropriate for the study of spidernet graphs and their moments, and the free Meixner law.

Balamurali Murugesan, Rukhshanda Hussain, Rajarshi Bhattacharya, Ismail Ben Ayed

Recently, CLIP-based approaches have exhibited remarkable performance on generalization and few-shot learning tasks, fueled by the power of contrastive language-vision pre-training. In particular, prompt tuning has emerged as an effective strategy to adapt the pre-trained language-vision models to downstream tasks by employing task-related textual tokens. Mo

Adam Larios, Collin Victor

We study the use of the Azouani-Olson-Titi (AOT) continuous data assimilation algorithm to recover solutions of the Navier--Stokes equations modified to have higher-order fractional diffusion. The fractional diffusion case is of particular interest, as it is known to be globally well-posed for sufficiently large diffusion exponent $\alpha$. In this work, we

Anton Bernshteyn, Abhishek Dhawan

We show that every Borel graph $G$ of subexponential growth has a Borel proper edge-coloring with $\Delta(G) + 1$ colors. We deduce this from a stronger result, namely that an $n$-vertex (finite) graph $G$ of subexponential growth can be properly edge-colored using $\Delta(G) + 1$ colors by an $O(\log^\ast n)$-round deterministic distributed algorithm in the

Søren Taverniers, Svyatoslav Korneev, Christoforos Somarakis, Morad Behandish

The computation of damping rates of an oscillating fluid with a free surface in which viscosity is small and surface tension high is numerically challenging. A typical application requiring such computation is drop-on-demand (DoD) microfluidic devices that eject liquid metal droplets, where accurate knowledge of damping rates for the least-damped oscillation

D. I. Martínez Moreno, J. Negro, L. M. Nieto

Symmetries associated with the Hamiltonian describing bilayer graphene subjected to a constant magnetic field perpendicular to the plane of the bilayer are calculated using polar coordinates. These symmetries are then applied to explain some fundamental properties, such as the spectrum and the integer pseudo-spin character of the eigenfunctions. The probabil

Melody Huang, Dan Soriano, Samuel D. Pimentel

Sensitivity to unmeasured confounding is not typically a primary consideration in designing treated-control comparisons in observational studies. We introduce a framework allowing researchers to optimize robustness to omitted variable bias at the design stage using a measure called design sensitivity. Design sensitivity, which describes the asymptotic power

Jane M. Lange, Kemal C. Gogebakan, Roman Gulati, Ruth Etzioni

Multi-cancer early detection (MCED) tests offer to screen for multiple types of cancer with a single blood sample. Despite their promising diagnostic performance, evidence regarding their population benefit is not yet available. Expecting that benefit will derive from detecting cancer before it progresses to an advanced stage, we develop a general two-stage

Yeqin Liu

We show that when a K3 surface acquires a node, the existence of stable spherical sheaves of certain Chern classes can be obstructed.

Claudio Cremaschini, Massimo Tessarotto

The problem posed by the possible existence/non-existence of spatially non-symmetric kinetic equilibria has remained unsolved in plasma theory. For collisionless magnetized plasmas this involves the construction of stationary solutions of the Vlasov-Maxwell equations. In this paper the issue is addressed for non-relativistic plasmas both in astrophysical and

Ciera McFarland, Margaret M. Coad

Soft, growing inflated beam robots, also known as everting vine robots, have previously been shown to navigate confined spaces with ease. Less is known about their ability to navigate three-dimensional open spaces where they have the potential to collapse under their own weight as they attempt to move through a space. Previous work has studied collapse of in

John Mark Agosta, Robert Horton

With the explosion of applications of Data Science, the field is has come loose from its foundations. This article argues for a new program of applied research in areas familiar to researchers in Bayesian methods in AI that are needed to ground the practice of Data Science by borrowing from AI techniques for model formulation that we term ``Decision Modellin

Jaume Llibre, Douglas D. Novaes, Claudia Valls

The generalized Chazy differential equation corresponds to the following two-parameter family of differential equations \begin{equation*}\label{gcdeq} \dddot x+|x|^q \ddot x+\dfrac{k |x|^q}{x}\dot x^2=0, \end{equation*} which has its regularity varying with $q$ , a positive integer. Indeed, for $q=1$ it is discontinuous on the straight line $x=0$, whereas fo

Alireza F. Behbahani, Vagelis Harmandaris

We analyze the displacements of the particles of a glass-forming molecular liquid perpendicular to a confining solid surface, using extensive molecular dynamics simulations with atomistic models. In the vicinity of an attractive surface, the liquid molecules are trapped. Transient localization of liquid molecules near the surface introduces a relaxation proc

Lianxin Zhang, Yihan Huang, Zhongzhong Cao, Yang Jiao

Parallel self-assembly is an efficient approach to accelerate the assembly process for modular robots. However, these approaches cannot accommodate complicated environments with obstacles, which restricts their applications. This paper considers the surrounding stationary obstacles and proposes a parallel self-assembly planning algorithm named SAPOA. With th

Milind N. Kunchur

This work reviews the human auditory system, elucidating some of the specialized mechanisms and non-linear pathways along the chain of events between physical sound and its perception. Customary relationships between frequency, time, and phase--such as the uncertainty principle--that hold for linear systems, do not apply straightforwardly to the hearing proc

Juha Tiirola

In this paper, a parts based loss is considered for finetune registering knee joint areas. Here the parts are defined as abstract feature vectors with location and they are automatically selected from a reference image. For a test image the detected parts are encouraged to have a similar spatial configuration than the corresponding parts in the reference ima

Axel Lazzarotto, Alain Hui-Bon-Hoa, Michel Rieutord

Intermediate-mass stars are often fast rotators, and hence are centrifugally flattened and affected by gravity darkening. To analyse this kind of stars properly, one must turn to 2D models to compute the visible radiative flux and to take the geometrical effect of the star inclination into account. Assuming a given stellar age and chemical composition, we ai

Maciej Pieczarka, Marcin Gębski, Aleksandra N. Piasecka, James A. Lott

Many bosons can occupy a single quantum state without a limit. This state is described by quantum-mechanical Bose-Einstein statistics, which allows the formation of a Bose-Einstein condensate at low temperatures and high particle densities. Photons, historically the first considered bosonic gas, were late to show this phenomenon, which was observed in rhodam

Stefan Hill, David Fitzek, Patrick Delfmann, Carl Corea

Regardless of the domain, forecasting the future behaviour of a running process instance is a question of interest for decision makers, especially when multiple instances interact. Fostered by the recent advances in machine learning research, several methods have been proposed to predict the next activity, outcome or remaining time of a process automatically

R. Channing Moore, Daniel P. W. Ellis, Eduardo Fonseca, Shawn Hershey

Machine learning from training data with a skewed distribution of examples per class can lead to models that favor performance on common classes at the expense of performance on rare ones. AudioSet has a very wide range of priors over its 527 sound event classes. Classification performance on AudioSet is usually evaluated by a simple average over per-class m